Predicting when a user is in need of a loan and notifying the user of loan offers

ABSTRACT

Techniques are disclosed to determine when a user is in need of a loan and notifying the user of loan offers. With user permission or affirmative consent, user data may be monitored for several users, which is used to build a user profile for each user. The user profile may then be analyzed to determine whether a user will require a loan within a future time period. To do so, the user data may include data from various sources, which indicate the user&#39;s interactions and behaviors such as demographic data, data indicative of user shopping habits, online browsing, life events, or other relevant behaviors. This data may then be analyzed to predict a statistical likelihood that a user will need a loan. When this statistical likelihood is exceeded, a user may be preapproved for a loan and/or a targeted notification may be sent indicating offers for certain types of loans.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to (1) Provisional Application No. 62/338,749, entitled “Using Cognitive Computing To Customize Loans,” filed on May 19, 2016; (2) Provisional Application No. 62/332,226, entitled “Using Cognitive Computing To Provide a Personalized Banking Experience,” filed on May 5, 2016; (3) Provisional Application No. 62/338,752, entitled “Using Cognitive Computing To Provide a Personalized Banking Experience,” filed on May 19, 2016; and (4) Provisional Application No. 62/341,677, entitled “Using Cognitive Computing To Improve Relationship Pricing,” filed on May 26, 2016, each of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to providing an improved banking experience and, more particularly, to predicting a user's loan needs and proactively notifying the user of loan offers.

BACKGROUND

Traditionally, to apply for various types of loans, people would visit a brick-and-mortar bank or other financial institution to meet with a loan officer or other representative, submit the necessary documentation and, if approved, sign a loan agreement defining the loan specifics, such as the loan term and rate. More recently, the loan application and approval process has been simplified by providing users with online options to apply, and potentially be approved for, various types of loans. Therefore, because of the ease in which people may apply for loans and compare interest rates among different lenders, the competition amongst lenders has dramatically increased.

This competition has increased the importance of advertising to users and identifying potential clients that may require loans. However, typical methods of doing so are often arduous and time consuming, and may include procedures such as manual review of client information, phone calls to clients or potential clients, and sending out promotional materials or mailers via the postal service. Therefore, the identification of clients that may require loans is valuable but may require time and resources that may not be recouped by the lender.

BRIEF SUMMARY

In one aspect, a computer-implemented method for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The method may include one or more processors (and/or associated transceivers) (1) receiving user input data associated with a user; (2) generating a user profile based upon the user input data and being indicative of whether the user will require a loan within the future time period based upon the user profile; (3) calculating a statistical likelihood of the user requiring the loan within a future time period based upon the user profile; (4) determining whether the statistical likelihood exceeds a threshold likelihood; and/or (5) when the statistical likelihood exceeds a threshold likelihood, transmitting a notification to a computing device associated with the user including offers for one or more customized loans. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

In yet another aspect, a system for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The system may include (1) a client device associated with a user, which may be configured to periodically transmit user input data associated with the user; and (2) one or more back-end components configured to (i) receive the user input data; (ii) generate a user profile based upon the received user input data and being indicative of whether the user will require a loan within a future time period; (iii) calculate a statistical likelihood of the user requiring a loan within a future time period based upon the user profile; (iv) determine whether the statistical likelihood exceeds a threshold likelihood; and/or (v) when the statistical likelihood exceeds a threshold likelihood, transmit a notification to the client device including offers for one or more customized loans. The system may include additional, less, or alternate components, including those discussed elsewhere herein.

In still another aspect, a computer-implemented method for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The method may include one or more processors (and/or associated transceivers) (1) receiving user input data associated with a user from a mobile device via wireless communication or data transmission over one or more radio links or wireless communication channels; (2) generating a user profile based upon the user input data, the user profile including data indicative of whether the user will require a loan within a future time period; (3) calculating a statistical likelihood of the user requiring the loan within the future time period based upon the user profile; (4) determining whether the statistical likelihood exceeds a threshold likelihood; and/or (5) when the statistical likelihood exceeds a threshold likelihood, transmitting a notification to the mobile device associated with the user via wireless communication or data transmission over one or more radio links or wireless communication channels. The notification may include, for example, offers for one or more customized loans. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

In an additional aspect, a computer-implemented method for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The method may include one or more processors (and/or associated transceivers) (1) receiving user input data associated with a user from a user mobile device via wireless communication or data transmission over one or more radio links or wireless communication channels; (2) generating a user profile based upon the user input data and being indicative of whether the user will require a loan within a future time period; (3) calculating a statistical likelihood of the user requiring the loan within the future time period based upon the user profile; (4) determining whether the statistical likelihood exceeds a threshold likelihood; and/or (5) when the statistical likelihood exceeds a threshold likelihood, transmitting a notification to the user mobile device for one or more loan offers via wireless communication or data transmission over one or more radio links or wireless communication channels to facilitate providing customized loans. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

In yet another aspect, a system for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The system may include (1) a mobile device associated with a user, the mobile device configured to periodically transmit user input data associated with the user; and (2) one or more back-end components configured to (i) receive the user input data transmitted by the mobile device via wireless communication or data transmission over one or more radio links or wireless communication channels; (ii) generate a user profile based upon the received user input data and being indicative of whether the user will require a loan within a future time period; (iii) calculate a statistical likelihood of the user requiring a loan within the future time period based upon the user profile; (iv) determine whether the statistical likelihood exceeds a threshold likelihood; and/or (v) when the statistical likelihood exceeds a threshold likelihood, transmit a notification to the user mobile device for user review via wireless communication or data transmission over one or more radio links or wireless communication channels, the notification detailing one or more loan offers, to facilitate providing customized loans. The system may include additional, less, or alternate components, including those discussed elsewhere herein.

Advantages will become more apparent to those of ordinary skill in the art from the following description of the preferred aspects which have been shown and described by way of illustration. As will be realized, the present aspects may be capable of other and different aspects, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an exemplary loan prediction and notification system 100 in accordance with one aspect of the present disclosure;

FIG. 2 illustrates exemplary user profiles 200 in accordance with one aspect of the present disclosure;

FIG. 3 illustrates exemplary logic diagrams 300 indicating the occurrence of several example conditions and their corresponding impact on various predictions in accordance with one aspect of the present disclosure; and

FIG. 4 illustrates an exemplary computer-implemented method flow 400 in accordance with one aspect of the present disclosure.

The Figures depict aspects of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternate aspects of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.

DETAILED DESCRIPTION

The present aspects relate to, inter alia, using cognitive computing and/or predictive modeling (and/or machine learning techniques or algorithms) to identify when one or more users may be looking for, or in need of, a loan. To accomplish this, user input data may be collected (with user permission or affirmative consent) and stored by one or more back-end components and used to construct a user profile. This data may include, for example, demographic data associated with the user such as the user's age, the age or birthdate of each member of the user's family, financial data, location data, and/or data indicative of various life events. In addition, the one or more back-end components may collect other types of data, such as data indicative of the user's behavior, including online browsing history, spending habits, etc. Using any portion of this data, a statistical likelihood may be calculated regarding whether the user requires or is actively looking for a loan. If this statistical likelihood exceeds a threshold value, then the one or more back-end components may transmit a targeted notification to the user indicating various loan offers.

System Overview

FIG. 1 is a block diagram of an exemplary loan prediction and notification system 100 in accordance with one aspect of the present disclosure. In the present aspect, loan prediction and notification system 100 may include one or more client devices 102, a loan prediction and notification engine 120, one or more financial institutions 150, and/or a communication network 116. Loan prediction and notification system 100 may include additional, less, or alternate components, including those discussed elsewhere herein.

For the sake of brevity, loan prediction and notification system 100 is illustrated as including a single client device 102, a single loan prediction and notification engine 120, two financial institutions 150, and a single communication network 116. However, the aspects described herein may include any suitable number of such components. For example, loan prediction and notification engine 120 may communicate with several client devices 102, each of which may be operated by a separate user, to receive data from each separate client device 102 and/or to transmit notifications to each separate client device 102, as further discussed herein.

To provide another example, loan prediction and notification engine 120 may receive data from one or more client devices 102 such that a user profile for each user may include data received from each user's respective client device. To provide yet another example, client device 102 may represent one client device from several different client devices for the same user or for different users. For example, client device 102 may represent a user's smartphone as well as a user's desktop computer, each of which may collect and transmit data to one or more financial institutions 150 and/or loan prediction and notification engine 120, as further discussed below.

Communication network 116 may be configured to facilitate communications between one or more client devices 102, one or more financial institutions 150, and/or loan prediction and notification engine 120 using any suitable number of wired and/or wireless links, such as links 117.1-117.3, for example. For example, communication network 116 may include any suitable number of nodes, radio frequency links, wireless or digital communication channels, additional wired and/or wireless networks that may facilitate one or more landline connections, internet service provider (ISP) backbone connections, satellite links, public switched telephone network (PSTN), etc.

To facilitate communications between the various components of loan prediction and notification system 100, the present aspects include communication network 116 being implemented, for example, as a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), or any suitable combination of local and/or external network connections. To provide further examples, communications network 116 may include wired telephone and cable hardware, satellite, cellular phone communication networks, base stations, macrocells, femtocells, etc. In the present aspects, communication network 116 may provide one or more client devices 102 with connectivity to network services, such as Internet services, for example, and/or support application programming interface (API) calls between one or more client devices 102, one or more financial institutions 150, and/or loan prediction and notification engine 120.

Client device 102 may be configured to communicate using any suitable number and/or type of communication protocols, such as Wi-Fi, cellular, BLUETOOTH, NFC, RFID, etc. For example, client device 102 may be configured to communicate with communication network 116 using a cellular communication protocol to send data to and/or receive data from one or more financial institutions 150 and/or loan prediction and notification engine 120 via communication network 116 using one or more of radio links or radio frequency links 117.1-117.3 or wireless communication channels.

In various aspects, client device 102 may be implemented as any suitable communication device. For example, client device 102 may be implemented as a user equipment (UE) and/or client device, such as a smartphone or other suitable mobile computing device, for example. To provide additional examples, client device 102 may be implemented as a personal digital assistant (PDA), a desktop computer, a tablet computer, a laptop computer, a wearable electronic device, etc.

As further discussed below, data collected and/or transmitted by client device 102 to one or more financial institutions 150 and/or loan prediction and notification engine 120 may include, for example, any suitable or relevant information used by loan prediction and notification engine 120 to track a location of client device 102 and/or to track other types of information about users associated with client device 102 to anticipate whether the user associated with client device 102 may require a loan. Additionally or alternatively, data collected and/or transmitted by client device 102 to one or more financial institutions 150 and/or loan prediction and notification engine 120 may include data used by loan prediction and notification engine 120 to preapprove a user for a loan and/or to calculate an adjusted loan rate or other loan specifics for a particular user.

For example, with user permission, such as user opt-in to a rewards or other program that provides financial benefits or cost savings to the user, client device 102 may collect, monitor, store, and/or transmit demographic information, data indicative of the user's behavior such as spending habits, where the user has shopped physically and/or online, data indicative of certain life events, financial information such as account balances of one or more users associated with client device 102, online web browsing history, life event data, etc. This data is discussed in more detail below with reference to FIG. 2.

Furthermore, data received by client device 102 from loan prediction and notification engine 120 may include any suitable information used to notify the user of relevant loan offers, the result of a loan preapproval, whether the user qualifies for a particular loan product and, if so, the particular loan products the user may qualify for. Additionally or alternatively, data received by client device 102 from loan prediction and notification engine 120 may facilitate providing user notifications regarding specific loan terms and/or loan rates, whether a particular loan includes personalized rates or other terms, and the specific details about how the customized loan details were adjusted for that user.

For example, if it is determined by loan prediction and notification engine 120 that a user associated with client device 102 is likely to require a loan, then client device 102 may display a notification transmitted via loan prediction and notification engine 120 regarding loan offers of a specific loan type and amount before the user has obtained the loan. To provide another example, if a user applies for a loan via client device 102 or in another manner, loan prediction and notification engine 120 may calculate user-specific loan specifics that take into account changes in the user's statistical risk during the loan term, which is further discussed below. Assuming the loan is approved, loan prediction and notification engine 120 may adjust the initial loan specifics based upon this additional information and transmit this information to client device 102, which may in turn display a suitable notification including the details of the personalized loan specifics.

Detailed Operation of Loan Customization System

In the present aspects, client device 102 may include one or more processors 104, a communication unit 106, a user interface 108, a display 110, a location acquisition unit 112, and a memory unit 114.

Communication unit 106 may be configured to facilitate data communications between client device 102 and one or more of communication network 116, one or more financial institutions 150, and/or loan prediction and notification engine 120 in accordance with any suitable number and/or type of communication protocols. In the present aspects, communication unit 106 may be configured to facilitate data communications based upon the particular component and/or network with which client device 102 is communicating.

Such communications may facilitate the transmission of collected data from client device 102 that is utilized by loan prediction and notification engine 120 to provide loan customization and/or to predict when a user associated with client device 102 is likely in the market for a new loan, as further discussed herein. In the present aspects, communication unit 106 may be implemented with any suitable combination of hardware and/or software to facilitate this functionality. For example, communication unit 106 may be implemented with any suitable number of wired and/or wireless transceivers, network interfaces, physical layers (PHY), ports, antennas, etc.

User interface 108 may be configured to facilitate user interaction with client device 102. For example, user interface 108 may include a user-input device such as an interactive portion of display 110 (e.g., a “soft” keyboard displayed on display 110), an external hardware keyboard configured to communicate with client device 102 via a wired or a wireless connection (e.g., a BLUETOOTH keyboard), an external mouse, or any other suitable user-input device.

Display 110 may be implemented as any suitable type of display that may facilitate user interaction, such as a capacitive touch screen display, a resistive touch screen display, etc. In various aspects, display 110 may be configured to work in conjunction with user-interface 108 and/or one or more processors 104 to detect user inputs upon a user selecting a displayed interactive icon or other graphic, to identify user selections of objects displayed via display 110, to display notifications regarding available loan offers, preapproval results, the terms of a particular customized loan, which may be received, for example, via loan prediction and notification engine 120, etc.

Location acquisition unit 112 may be implemented as any suitable device configured to generate location data indicative of a current geographic location of client device 102. In one aspect, location acquisition unit 102 may be implemented as a satellite navigation receiver that works with a global navigation satellite system (GNSS) such as the global positioning system (GPS) primarily used in the United States, the GLONASS system primarily used in the Russian Federation, the BeiDou system primarily used in China, and/or the Galileo system primarily used in Europe.

Location acquisition unit 112 and/or one or more processors 104 may be configured to receive navigational signals from one or more satellites and to calculate a geographic location of client device 102 using these signals. Location acquisition unit 112 may include one or more processors, controllers, or other computing devices and memory to calculate the geographic location of client device 102 without one or more processors 104. Alternatively, location acquisition unit 112 may utilize components of one or more processors 104. Thus, one or more processors 104 and location acquisition unit 112 may be combined or be separate or otherwise discrete elements.

One or more processors 104 may be implemented as any suitable type and/or number of processors, such as a host processor for the relevant device in which client device 102 is implemented, for example. One or more processors 104 may be configured to communicate with one or more of communication unit 106, user interface 108, display 110, location acquisition unit 112, and/or memory unit 114 to send data to and/or to receive data from one or more of these components.

For example, one or more processors 104 may be configured to communicate with memory unit 114 to store data to and/or to read data from memory unit 114. In accordance with various embodiments, memory unit 114 may be a computer-readable non-transitory storage device, and may include any combination of volatile (e.g., a random access memory (RAM)), or a non-volatile memory (e.g., battery-backed RAM, FLASH, etc.). In the present aspects, memory unit 114 may be configured to store instructions executable by one or more processors 104. These instructions may include machine readable instructions that, when executed by one or more processors 104, cause one or more processors 104 to perform various acts.

In the present aspects, loan application 115 is a portion of memory unit 114 configured to store instructions, that when executed by one or more processors 104, cause one or more processors 104 to perform various acts in accordance with applicable aspects as described herein. For example, instructions stored in loan application 115 may facilitate one or more processors 104 performing functions such as periodically reporting and/or transmitting the location of client device 102 as part of a running background process (or causing location acquisition unit 112 to do so), collecting various types of data, sending various types of data to one or more financial institutions 150 and/or loan prediction and notification engine 120, receiving data and/or notifications from one or more financial institutions 150 and/or loan prediction and notification engine 120, displaying notifications and/or other information using data received via one or more financial institutions 150 and/or loan prediction and notification engine 120, etc.

In some aspects, loan application 115 may reside in memory unit 114 as a default application bundle that may be included as part of the operating system (OS) utilized by client device 102. But in other aspects, loan application 115 may be installed on client device 102 as one or more downloads, such as an executable package installation file downloaded from a suitable application source via a connection to the Internet or other suitable device, network, external memory storage device, etc.

For example, loan application 115 may be stored in any suitable portions of memory unit 114 upon installation of a package file downloaded in such a manner. Examples of package download files may include downloads via the iTunes store, the Google Play Store, the Windows Phone Store, a package installation file downloaded from another computing device, etc. Once downloaded, loan application 115 may be installed on client device 102 as part of an installation package such that, upon installation of loan application 115, memory unit 114 may store executable instructions such that, when executed by one or more processors 104, cause client device 102 to implement the various functions of the aspects as described herein.

The user may initially create a user profile upon first launching loan application 115, through a registration process via a website, over the phone, etc. This user profile may include, for example, the customer's demographic information or any suitable information that may be useful in facilitating various portions of the aspects as described herein. For example, upon installing and launching loan application 115 on client device 102, a user may be prompted to enter login information and/or complete an initial registration process to create a user profile with a lender or other relevant party associated with or otherwise affiliated with loan prediction and notification engine 120.

Additionally or alternatively, loan application 115 may periodically request data directly from the user as opposed to collecting data in a passive manner. For example, loan application 115 may request certain types of information from the user as part of one or more surveys and/or questionnaires. This information may be requested, for example, upon an initial registration and/or at any suitable time after the initial registration. In this way, a user may be asked for certain types of information that may be difficult to obtain through third party data providers and/or public records. For example, a user may be asked about the ages of his family members, his current income level (or where his earnings fit within a range of earnings), his hobbies and interests, whether he is planning to attend a university or other training program in the near future and, if so, in what field of study, whether he intends to make a large purchase in the near future, etc.

An initial registration process may additionally or alternatively include, for example, obtaining the user's consent to track her location or to otherwise collect and utilize other types of data to provide the various loan customization and/or prediction services as discussed herein. For example, a user may opt in to allow loan prediction and notification engine 120 to track and/or receive the user's financial account data including financial transactions, spending history, credit card balances, web browsing history, and/or account balances associated with the user's financial accounts at one or more financial institutions 150, to opt in to a system whereby the user's spending habits are tracked and/or spending data at various retailers is collected, to provide authorization (e.g., online login credentials) to access the user's accounts held at one or more financial institutions 150, etc. In return, the user may be presented with various offers or customized financial products or loan offers, or other products discussed herein.

In the present aspects, loan application 115 may provide different levels of functionality based upon options selected by a user and/or different implementations of loan application 115. For example, in some aspects, loan application 115 may facilitate client device 102 working in conjunction with one or more financial institutions 150 and/or loan prediction and notification engine 120 to predict when a user requires a loan and/or to send data relevant to these functions to loan prediction and notification engine 120. Furthermore, in accordance with such aspects, loan application 115 may facilitate receiving notifications for offers regarding loans anticipated by loan prediction and notification engine 120. To provide another example, other aspects include loan application 115 facilitating client device 102 working in conjunction with one or more financial institutions 150 and/or loan prediction and notification engine 120 to collect and transmit data to one or more financial institutions and/or loan prediction and notification engine 120.

One or more financial institutions 150 may include any suitable number and/or type of financial institutions that hold and/or are associated with various financial accounts. For example, one or more financial institutions 150 may include banks, creditors, lenders, and/or brokers. One or more users (e.g., a user associated with client device 102) may hold one or more accounts with one or more financial institutions 150 such as checking accounts, savings accounts, credit accounts, lines of credit, loan accounts, charge accounts, money market accounts, brokerage accounts, etc. These accounts may be held at a single institution or spread out across several different financial institutions.

In one aspect, financial accounts held at one or more financial institutions 150 may be accessible via a secure connection to communication network 116, for example, by client device 102 and/or loan prediction and notification engine 120. For example, one or more financial institutions 150 may provide online services that allow a user to access her accounts using client device 102 and/or another suitable computing device. Upon receipt of a valid authenticated request for financial data, one or more financial institutions 150 may transmit financial data to client device 102 and/or loan prediction and notification engine 120. Examples of the financial data transmitted by one or more financial institutions 150 may include financial transaction data indicating previous credits and debits to a user's accounts, a current account balance, loan payoff balances, credit report history, credit scores, credit card utilization, derogatory credit marks, spending data such as the time, amount, and specific merchant for which previous account debits and/or charges were made, whether the user has previously defaulted on a particular loan, etc.

Loan prediction and notification engine 120 may be affiliated or otherwise associated with one or more parties, which may be the same party or a different party than those affiliated with one or more financial institutions 150. Loan prediction and notification engine 120 may include any suitable number of components configured to receive data from and/or send data to one or more of client devices 102 and/or one or more financial institutions 150 via communication network 116 using any suitable number of wired and/or wireless links. In various embodiments, loan prediction and notification engine 120 may constitute a portion of (or the entirety of) one or more back-end components, and may be configured (alone or in conjunction with other back-end components) to execute one or more applications to perform one or more functions associated with the various aspects as discussed herein.

For example, as shown in FIG. 1, loan prediction and notification engine 120 may communicate with one or more external computing devices such as servers, databases, database servers, web servers, etc. The present aspects include loan prediction and notification engine 120 working in conjunction with any suitable number and/or type of back-end components to facilitate the appropriate functions of the aspects as described herein.

In the present aspects, loan prediction and notification engine 120 may be implemented, for example, as any suitable number and/or type of servers configured to access data from one or more additional data sources and/or store data to one or more storage devices. For example, as shown in FIG. 1, additional data sources 170 may represent data that is made accessible by loan prediction and notification engine 120 from any number N of data sources, such as third-party data providers or other data sources in addition to and/or including one or more financial institutions 150. This additional data may be utilized by loan prediction and notification engine 120 to build a personalized user profile for each user, which may be stored in one or more suitable storage units (e.g., storage unit 180) and include other types of data, as further discussed below. Loan prediction and notification engine 120 may access each user's profile as part of the execution of one or more cognitive computing and/or predictive modeling algorithms to calculate a statistical likelihood that a particular user is interested in acquiring a loan and/or to determine a specific loan type and accompanying loan specifics for a particular user based upon that user's profile.

To provide some illustrative examples, additional data sources 170 may include, again with user permission, data mined from social media and/or user's web browsing habits (e.g., search terms and websites), data provided by various retailers, demographic data associated with other users in a particular region and/or associated with a particular retailer, income levels of various users, where different users commonly shop, assets associated with various users such as the cars each user owns, mortgage loan information associated with various users, how much users typically spend at various retailers, etc. To provide further examples, additional data sources 170 may include specific demographic information for a particular user and his family members, such as the age and gender of each child, for example.

To provide even more examples, additional data sources 170 may include data indicative of various user life events such as getting married, a child about to attend (or currently attending) college, paying off a previous loan, receiving a settlement or inheritance, etc. Still further, additional data sources 170 may include user data such as spending habits or detailed information such as a college or university the user may be attending, courses being taken by the user, a college major or other focused field of study, etc. Loan prediction and notification engine 120 may utilize any portion of such data (as well as data from other data sources) as input to one or more cognitive computing and/or predictive modeling algorithms to perform the predictions and statistical calculations as discussed herein, some examples of which are further discussed below.

Loan prediction and notification engine 120 may be implemented as any suitable number of servers that are configured to generate and/or store various user profiles in storage unit 180. Each user's profile may include, for example, an aggregation of aforementioned data and/or any other suitable data that may be utilized to predict whether a user is actively pursuing and/or requires a loan for a new item purchase and/or data which may be used to calculate loan terms associated with such predicted loans.

For example, a user's profile may include demographic data, data submitted by a user as part of an initial registration process, data submitted by a user in response to solicited surveys, spending data, financial data (e.g., credit card utilization, credit card balances, bank account balances, etc.), credit report data, credit scores, family or individual income, etc. Each user's profile may be stored in storage unit 180 in any suitable manner such that loan prediction and notification engine 120 may access each user's profile and correlate each user to his or her profile.

To provide an illustrative example, storage unit 180 may include a number of user profiles organized in accordance with suitable type of information to uniquely identify each particular user so that each user may later be matched to her profile stored in storage unit 180. For example, each user's profile may be identified by a username that is used by one or more users in accordance with loan application 115, a first and last name of each user, etc. These user profiles are discussed in further detail below with reference to FIG. 2.

In the present aspects, loan prediction and notification engine 120 may include one or more processors 122, a communication unit 124, and a memory unit 126. One or more processors 122, communication unit 124, and memory unit 126 may perform substantially similar functions as one or more processors 104, communication unit 106, and memory unit 114, respectively, of client device 102. Therefore, only differences between these components will be further discussed herein.

Of course, differences between components of loan prediction and notification engine 120 and client device 102 may be owed to differences in device implementation rather than the functions performed by each individual component. For example, if loan prediction and notification engine 120 is implemented as a server whereas client device 102 is implemented as a personal computing device, one or more processors 122 may have more processing power (e.g., a faster processor, more cores, etc.) than one or more processors 104, although one or more processors 122 may perform similar functions as one or more processors 104 (e.g., executing instructions stored in memory to perform various acts, processing data, etc.).

In the present aspects, loan prediction and notification engine 120 may be configured to send or otherwise transmit various types of notifications and/or inquiries to client device 102. These notifications and/or inquiries may be sent, for example, from loan prediction and notification engine 120 via communication unit 124, and may include any suitable type of data transmissions. For example, loan prediction and notification engine 120 may transmit appropriate notifications and/or inquiries via emails, text messages, push notifications, etc., to client device 102

Again, in various aspects, loan prediction and notification engine 120 may acquire data from various sources to facilitate the various aspects described herein. Some of these sources may include data from secure connections or may otherwise require secured or authorized access. For example, one or more financial institutions 150 may provide access to one or more user's financial account data via any suitable authentication techniques, such as via a secure connection, password authentication, public and/or private key exchanges, biometric identification, etc.

Therefore, in the present aspects, loan prediction and notification engine 120 may, when appropriate, implement any suitable techniques to obtain information in a legal and technically feasible manner. For example, as discussed above, a user may setup an account and/or profile with a third party associated with loan prediction and notification engine 120. As discussed above, the user may then opt in to data collection via the various data sources that are to be collected via loan prediction and notification engine 120. The user may additionally or alternatively provide authentication information for each account and/or data source for which data is to be accessed, collected, tracked, monitored, etc., such that loan prediction and notification engine 120 may obtain any suitable type of data to carry out the aspects described herein.

Cognitive Computing

Cognitive computing may refer to systems that learn at scale, reason with purpose, and/or interact with humans naturally. Rather than being explicitly programmed, cognitive computing systems may learn and reason from their interactions and from their experiences with their environment. As opposed to traditional computing systems, which are deterministic, cognitive computing systems are probabilistic. In other words, cognitive computing systems generate not just answers to numerical problems, but hypotheses, reasoned arguments, and recommendations about more complex and meaningful bodies of data. Cognitive computing systems may advantageously interpret and utilize data that is typically referred to as being unstructured in nature. This allows such systems to keep pace with the volume, complexity, and unpredictability of information and systems in the modern world. To do so, cognitive computing systems attempt to augment the reasoning and thought processes of the human brain.

Therefore, in various aspects, loan prediction and notification engine 120 may be implemented as a computing device (or a constituent part of one or more computing devices) that is/are configured to process data in accordance with one or more cognitive computing techniques. For example, loan prediction and notification engine 120 may be implemented as one or more nets or nodes of an artificial neural network system and/or other suitable system that models and/or mimics the reasoning and processing of the human brain. Thus, cognitive computing and predictive modeling application 127 may include one or more machine learning algorithms, code, logic, and/or instructions to facilitate the behavior, functionality, and/or processing of a cognitive computing system.

In the present aspects, cognitive computing and predictive modeling application 127 may include any suitable combination of functions as discussed herein. For example, as shown in FIG. 1, cognitive computing and predictive modeling application 127 may include a data aggregation module 129 and a loan prediction module 131. These modules are for illustrative purposes and represent examples of some of the functionality that may be performed by loan prediction and notification engine 120 in accordance with a cognitive computing-based system. However, aspects include cognitive computing and predictive modeling application 127 including additional, less, or alternate actions, including those discussed elsewhere herein. Furthermore, aspects include cognitive computing and predictive modeling application 127 implementing traditional, non-cognitive computing processes.

In one aspect, data aggregation module 129 may be a portion of memory unit 126 configured to store instructions, that when executed by one or more processors 122, cause one or more processors 122 to perform various acts in accordance with applicable embodiments as described herein. In the present aspects, instructions stored in data aggregation module 129 may facilitate one or more processors 122 performing functions such as mining data for any suitable number of users. For example, instructions stored in data aggregation module 129 may facilitate loan prediction and notification engine 120 providing the requested authorization to one or more financial institutions 150 and/or additional data sources 170 as needed and to receive data from one or more of these sources.

In the present aspects, instructions stored in data aggregation module 129 may facilitate loan prediction and notification engine 120 aggregating and organizing received data into one or more user profiles, which may then be stored in storage unit 180. Again, these user profiles may be associated with each user and organized such that data contained as part of each user's profile may be associated with each user. For example, each user's username and/or other suitable identifying information may be stored as part of a user profile that includes an aggregation of all data received for that particular user from one or more data sources, as previously discussed.

In one aspect, loan prediction module 131 may be a portion of memory unit 126 configured to store instructions, that when executed by one or more processors 122, cause one or more processors 122 to perform various acts in accordance with applicable aspects as described herein. In the present aspects, instructions stored in loan prediction module 131 may facilitate one or more processors 122 performing functions such as calculating a statistical likelihood of the user requiring a loan within a future time period based upon the user's profile. To do so, aspects include loan prediction and notification engine 120 accessing and/or receiving one or more inputs that may be stored as part of the user's profile.

One or more processors 122 may be configured to execute instructions stored in loan prediction module 131 to interpret, organize, weight, and/or analyze these inputs in accordance with any suitable cognitive computing and/or predictive modeling techniques. For example, instructions stored in loan prediction module 131 may facilitate one or more processors 122 weighting these inputs as part of a weighting function, and calculating, as an output of the weighting function, a statistical likelihood of the user requiring a loan within a future time period. To provide another example, instructions stored in loan prediction module 131 may facilitate one or more processors 122 making certain determinations and/or identifying certain types of behavior as being associated with the purchase of a particular item that will require a particular type of monetary loan. In addition, aspects include the use of different likelihood thresholds may be set based upon a desired tradeoff between accuracy and sensitivity such that, upon each threshold being exceeded, a particular triggering action is performed. For example, a threshold likelihood value A (e.g., 75%) being exceeded may result in a determination that the user requires a loan. To provide another example, a threshold likelihood value B (e.g., 80%) threshold being exceeded may result in the user being preapproved for a loan.

Furthermore, loan prediction and notification engine 120 may have access to different types of data, with some types of data better indicating a user's intention of requiring a loan than others. In some of the present aspects, these different inputs may be weighted accordingly, as discussed above. However, in other aspects, loan prediction and notification engine 120 may calculate the statistical likelihood of a user requiring a monetary loan (or other predictions) based upon certain logical conditions being satisfied, which may be based upon different types to data. That is, different portions of collected data aggregated as part of a user's profile may be analyzed such that, when certain types of information are present, certain logical conditions are considered to be satisfied. Some types of information may be considered such good indicators of a user's intention to obtain a loan that, if one or more of such logical conditions are satisfied, then a determination may be made that the user requires a loan, which may trigger one or more actions as discussed above. Further details of the implementation of both weighted calculations and logical condition calculations to determine the statistical likelihood of a user requiring a loan are further discussed below.

To provide an illustrative example, a statistical likelihood of a user requiring a loan within the next 60 days may be calculated that exceeds a threshold likelihood (e.g., 75%). To provide another illustrative example, one or more logical conditions may be satisfied that indicate that the user is actively looking for a loan within the next 30 days. In either event, loan prediction and notification engine 120 may transmit a notification to client device 102 indicating that several types of loans are available and their specifics. In this way, a user may be notified of potential loan offers from a particular lender even if the user has not actively solicited a loan from that lender.

In the present aspects, loan prediction and notification engine 120 may not only determine that a user is looking for a loan, but may utilize other sources of data to actively begin or otherwise simplify the loan application process for the user. For example, one or more processors 122 may be configured to execute instructions stored in loan prediction module 131 to prequalify and/or preapprove a user for a predicted loan that the user will likely soon require. To do so, one or more processors 122 may be configured to execute instructions stored in loan prediction module 131 to also predict other details associated with the loan such as a type of loan, a loan term, and a range of monetary amounts for which the user is statistically likely to require.

To provide an illustrative example, a user may perform online research for three different vehicles that are the same type of vehicle (e.g., a sedan), but from three different car manufacturers. Loan prediction and notification engine 120 may then receive this information from client device 102 in the form of search terms used and websites visited as part of the user's online research. Once it is determined that a user requires an auto loan (from the user's online history and/or additional data) loan prediction module 131 may facilitate the determination of a range of loan amounts associated with each vehicle that was most often researched. Continuing this example, loan prediction module 131 may facilitate the identification of a range of prices (e.g., via accessing data sources 170) or a manufacturer's suggested retail price (MSRP) and/or another range of prices commonly associated with purchasing the identified vehicles in that user's region. Once this information is known, the loan preapproval process may use the highest value from these ranges, for example, to preapprove the user for the maximum likely amount needed for the auto loan. This ranging procedure may also be used for any other suitable type of loan such as new mortgages, lines of credit, home equity loans, student loans, etc.

Of course, prequalification and/or preapproval processes may require other information from the user such as household income, contact information, household debt, etc. To the extent that additional information is needed to prequalify and/or preapprove a user for a particular type of loan, loan prediction and notification engine 120 may acquire such information in advance. For example, this information may be requested as part of the initial opt in and registration process, acquired via communication via one or more financial institutions 150 and/or data sources 170, requested form the user, etc. Additional information required for prequalification and/or preapproval may also include user consent, signatures, etc., which may be obtained via similar methods.

To provide another illustrative example, a user may perform online research to shop for a new home via one or more realtor websites or other websites often used for such purposes. As part of this process, the user may email or otherwise communicate with realtors in the area. Loan prediction and notification engine 120 may receive detailed information from client device 102 in the form of specific geographic regions searched, the specific type of homes searched, filters applied on particular websites associated with searches, key words used when communicating with realtors, etc. The present aspects include one or more processors 122 executing instructions stored in loan prediction module 131 to identify a range of home costs and a future time line for which the user is likely to purchase the home. Using this information, loan prediction and notification engine 120 may send the appropriate notification to the user indicating available loan offers for homes of a specific type and/or for a specific amount that the user may be (or already be) qualified for.

To provide yet another illustrative example, a user may perform online research regarding colleges or universities to attend and/or fill out online applications for such institutions. Loan prediction and notification engine 120 may receive detailed information from client device 102 in the form of specific institutions searched and, based upon the current time in the academic year, when classes start at the institutions. Loan prediction and notification engine 120 may also receive the applicant's current age and the average cost for the student to attend these institutions. Using this aggregated pool of information, loan prediction and notification engine 120 may calculate a range of tuition costs and time line for which the user is likely to require a student loan. Using this information, loan prediction and notification engine 120 may send the appropriate notification to the user indicating available student loan offers, amounts, and their specific of the loan offers.

Exemplary Calculations for Predicting Loan Needs Using Cognitive Computing

FIG. 2 illustrates exemplary user profiles 200 in accordance with one aspect of the present disclosure. As shown in FIG. 2, user profiles 200 are an example of the various types of data that may be stored in any suitable number of storage units or databases (e.g., storage unit 180, as shown in FIG. 1). User profiles 200 may be generated, organized, modified, and/or accessed via a personalized loan engine, such as loan prediction and notification engine 120, for example, as shown in FIG. 1.

As shown in FIG. 2, user profiles 200 may include a number of different types of collected data associated with a number of individual users A-D. FIG. 2 illustrates four exemplary user profiles and five different types of data associated with each user profile. However, the present aspects include user profiles 200 including any suitable number of user profiles for any suitable number of different users, which may be associated with any suitable number and/or type of data.

User profiles 200 illustrate a number of different types of data associated with each user. The different types of data aggregated as part of each user's financial profile may be collectively referred to as “user input data,” although the user input data may include information about the user as well as other people, such as the user's family members. Again, this user input data may be used in accordance with the present aspects to perform calculations and predictions regarding whether a particular user will soon require a loan and/or the calculation of specific customized loan specifics.

As shown in FIG. 2, each user's financial profile contains different types of information, and portions of each respective type of information may represent user inputs for cognitive computing and predictive modeling application 127. Inputs (a1-a6) may represent each user's demographic data, such as the user's individual age and/or the ages of each member of the user's family (e.g., their birthdates), gender, marital status, household size, whether the user owns a home or rents, the user's ethnicity, the ethnicity of members of the user's family, etc.

Inputs (b1-b3) may represent different types of financial data such as, for example, the user's current individual and/or family annual income, the user's potential individual and/or potential family annual income (which may be calculated from other portions of the user's financial profile, as previously discussed), the user's credit information such as credit card utilization, debt-to-income ratios, credit scores, credit reports, etc.

Inputs (c1-c3) may represent various types of behavioral data that indicate the user's previous and likely future choices, behaviors, and/or actions. For example, behavioral data may represent online data, online search terms, the content of email such as various identified key words and their frequency of use, browsing history such as various websites visited by the user, etc.

To provide an illustrative example, behavioral data may indicate that the user recently researched different makes and models of cars by visiting several car review websites, by entering online search terms for specific types of vehicles, and/or by visiting automotive manufacturer websites. To provide additional illustrative examples, the behavioral data may indicate that a user has visited different lending websites seeking specific types of loans. To provide yet another illustrative example, the behavioral data may indicate that the user has visited several real-estate based websites looking to purchase a home and the range of home values searched. To provide even further illustrative examples, behavioral data may indicate that a user has completed an online application for a university, has researched options for student loans, and/or has visited certain college or university websites.

Inputs (d1-d6) may represent various types of life event data that may indicate if and when the user is ready to acquire a new loan. The life event data may include different types of data from public records, data submitted and/or collected from the user, and/or data collected via other third party sources. As shown in FIG. 2, the life event data may include information such as birth records (e.g., birth certificates recently recorded), marriage certificates, an indication that a child has started college, a determination of whether a child is approaching legal driving age, whether the user has recently purchased a new home, an indication that a user's current home is a particular age that may require repairs (and thus the user may be potentially interested in a home equity loan), etc.

Inputs (e1-e2) may represent different types of location data that may indicate the user's current geographic location and/or a history of the user's previous locations. In the present aspects, this location data may be analyzed and/or referenced to other locations known to be associated with a user actively seeking or otherwise likely to require a monetary loan. To provide an illustrative example, the location data may indicate that the user recently visited physical geographic locations associated with car dealerships, college campuses, physical banks or lender locations, high-end retailers, etc.

Again, in the present aspects, loan prediction and notification engine 120 may actively collect, aggregate, and/or monitor data representing user inputs for each user's profile to calculate a statistical likelihood of the user requiring a loan, as discussed herein. To perform these tasks, the present aspects include loan prediction and notification engine 120 analyzing the user input data in accordance with any suitable predictive modeling and/or cognitive computing techniques. For example, loan prediction and notification engine 120 may calculate the statistical likelihood of the user requiring a loan within some future time period as an output of a weighting function that weights a plurality of user inputs extracted from any combination of data stored in a user's financial profile. The weighting function may, for example, place weights upon various user inputs that tend to contribute or correlate more to the determination of whether a user will likely require a loan.

This may be analyzed, for example, in light of previous data stored in the user's financial profile and/or a history of data stored in other user's financial profiles. To provide an illustrative example, in the case of predicting whether a user is likely to require a car loan in the next 30 days, an analysis of data stored across one or more user profiles may indicate a strong correlation between a child turning legal driving age and the child's parents, within some period of time thereafter, purchasing a vehicle and obtaining a loan for that vehicle.

Exemplary Logic Diagram for Cognitive Computing Predictions

FIG. 3 illustrates exemplary logic diagrams 300 indicating the occurrence of several example conditions and their corresponding impact on various predictions in accordance with an aspect of the present disclosure. As shown in FIG. 3, each of examples 1-3 is based upon different portions of user input data stored as part of user profiles associated with three different users A-C. The user profiles shown in FIG. 3 may be implementations of user profiles 200, for example, as shown in FIG. 2.

As discussed above, various aspects include some user inputs being weighted in any suitable manner to predict when a user may require a loan, the specific type of loan, and when it is likely to be needed. Some user inputs, however, may be of particular importance such that, when treated individually or in combination with other user inputs, they satisfy a logical condition that is associated with various different types of loans. For example, as shown in Example 1 of FIG. 3, user A's profile may reveal the age of a user A's child based upon that child's birthdate and the current date. Using this information, loan prediction and notification engine 120 may determine a number of days until the child turns a legal driving age from the current date. A logical condition may be setup such that, once this number of days is equal to or less than a minimum future time period (e.g., 90 days, as shown in Example 1), the statistical likelihood of the user purchasing a car within this time period (or some time period thereafter) exceeds a threshold likelihood.

That is, the logical condition, when satisfied, may set the statistical likelihood to 100% to ensure that logical condition, when satisfied, results in the occurrence of specific types of actions and/or notifications. In this way, once a logical condition has been satisfied, a number of associated loan offers corresponding to that satisfied logical condition may be generated. For example, as shown in FIG. 3 for Example 1, three different loan offers may be calculated and sent to user A for various interest rates, amounts, and/or loan terms once the logical conditions have been met.

To provide another example, Example 2 of FIG. 3 illustrates user inputs that indicate a user B has recently married and never owned a home. In this scenario, a logical condition may be satisfied to trigger the calculation of various home mortgage loans for a first time homebuyer, which may include additional incentives such as lower interest rates and/or require less money down. In this way, the user B's specific inputs may be considered such that, when a specific logical condition is satisfied based upon these inputs, specific types of loans may be calculated and these offers sent to user B.

To provide yet another example, Example 3 of FIG. 3 illustrates user inputs indicating that a user C recently had a new baby, that the family's size with the new child is 4 total people, and that user C's current home has two total bedrooms. Using this information, a logical condition may be satisfied since it is likely that, based upon these factors, that the user C will be looking to purchase a new home to accommodate this larger family size. In doing so, loan prediction and notification engine 120 may calculate specific mortgage loan offers as shown in FIG. 3 and send these offers to user C.

For brevity, the Examples shown in FIG. 3 and previously described illustrate logical conditions satisfied by one to three user inputs. However, the present aspects include any suitable number of user inputs or other factors being considered as part of the determination of whether particular conditions have been satisfied. Furthermore, aspects also include a trigger condition causing the calculation of various loans resulting from more than one logical condition being satisfied. For example, logical conditions may be satisfied when the user has been married for more than 30 days, when a newborn baby is 45 days old, etc.

Exemplary Computer-Implemented Method of Predicting when a User is in Need of a Loan and Notifying the User of Loan Offers

FIG. 4 illustrates an exemplary computer-implemented method flow 400 in accordance with an aspect of the present disclosure. In the present aspects, one or more portions of method 400 (or the entire method 400) may be implemented by any suitable device, and one or more portions of method 400 may be performed by more than one suitable device in combination with one another. For example, one or more portions of method 400 may be performed by client device 102 and/or loan prediction and notification engine 120, as shown in FIG. 1. In one embodiment, method 400 may be performed by any suitable combination of one or more processors, instructions, applications, programs, algorithms, routines, etc. For example, method 400 may be performed via or more processors 122 executing instructions stored in cognitive computing and predictive modeling application 127 in conjunction with data collected, received, and/or generated via loan prediction and notification engine 120, such as user input data discussed herein, for example.

Method 400 may start when one or more processors receive user input data (block 402). In the present aspects, the user input data may be received from any suitable number and/or type of data sources. For example, the user input data may correspond to data collected via client device 102, one or more financial institutions 150, and/or one or more additional data sources 170, as shown and discussed with reference to FIG. 1. To provide another example, the user input data may include one or more of types of information received and used to generate a user's profile, as shown and discussed with reference to FIG. 2.

Method 400 may include one or more processors generating and/or monitoring a user profile based upon the user input data (block 404). This may include, for example, a loan prediction and notification engine (e.g., loan prediction and notification engine 120) organizing, aggregating, and/or storing the various different types of user input data to a storage unit (block 404). This may also include, for example, a loan prediction and notification engine continuously and/or periodically monitoring the contents of a user's profile for changes (block 404).

Method 400 may include one or more processors calculating a statistical likelihood of the user requiring a loan within a future time period (block 406). This may include, for example calculating the statistical likelihood based upon any suitable cognitive computing techniques that may analyze the user input data and draw conclusions from this analysis (block 406). This may also include, for example, calculating the output of a weighting function or other suitable predictive function that utilizes one or more of the user inputs, as further discussed herein (block 406). Still further, this may also include, for example, calculating or otherwise determining whether one or more logical conditions that are associated with the user potentially requiring a loan have been satisfied, as further discussed herein (block 406).

Method 400 may include one or more processors determining whether the calculated statistical likelihood of the user requiring a loan (block 406) exceeds a threshold likelihood (block 408). In some aspects, this may include the determination of whether the statistical likelihood, which may be calculated as the output of a suitable predictive function (block 406), exceeds a threshold likelihood (block 408). In other aspects, this determination may include determining whether one or more logical conditions have been satisfied, which alternatively may be viewed as the statistical likelihood being set to 100% or some other value exceeding a threshold likelihood (block 408). If so, then method 400 may continue (block 410). If not, then method 400 may revert back to continuing to monitor the user input data (block 404).

Method 400 may include one or more processors transmitting a notification to a user's client device indicating one or more loan offers the user likely needs (block 410). Again, these notifications may include, for example, push notifications, email messages, text messages, etc. (block 410). These loan offers may include, for example, the details associated with one or more customized loans that are generated once it has been determined (block 408) that the user likely requires a loan (block 410). For example, the loan offers indicated in the transmitted notification could include those discussed herein with reference to FIG. 3, whereby various auto loan offers and mortgage loan offers are determined based upon each user's specific user profile.

Cognitive Computing & Machine Learning

The cognitive computing and/or predictive modeling techniques discussed herein may include machine learning techniques or algorithms. For instance, customer data may be input into machine learning programs may be trained to (i) determine a statistical likelihood that a customer is looking for a loan, or may default on a loan, (ii) customize a loan product, and/or (iii) predict a life event, based upon the customer data, such as the types of customer data discussed elsewhere herein.

In certain embodiments, the cognitive computing and/or predictive modeling techniques discussed herein may heuristic engine and algorithms, and/or machine learning, cognitive learning, deep learning, combined learning, and/or pattern recognition techniques. For instance, a processor or a processing element may be trained using supervised or unsupervised machine learning, and the machine learning program may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs.

Additionally or alternatively, the machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as de-personalized customer data, image, mobile device, insurer database, and/or third-party database data. The machine learning programs may utilize deep learning algorithms that may be primarily focused on pattern recognition, and may be trained after processing multiple examples. The machine learning programs may include Bayesian program learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing—either individually or in combination. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the processing element may be required to find its own structure in unlabeled example inputs. In one embodiment, machine learning techniques may be used to identify potential loan applicants and customize loan products for individual customers.

In one embodiment, a processing element (and/or heuristic engine or algorithm discussed herein) may be trained by providing it with a large sample of images and/or user data with known characteristics or features. Based upon these analyses, the processing element may learn how to identify characteristics and patterns that may then be applied to analyzing user device details, user request or login details, user device sensors, geolocation information, image data, the insurer database, a third-party database, and/or other data. For example, the processing element may learn, with the user's permission or affirmative consent, to identify the user and/or the asset that is to be the subject of a transaction, such as generating an insurance quote or claim, opening a financial account, handling a loan or credit application, processing a financial (such as a credit card) transaction or the like.

Technical Advantages

The aspects described herein may be implemented as part of one or more computer components such as a client device and/or one or more back-end components, such as a loan prediction and notification engine, for example. Furthermore, the aspects described herein may be implemented as part of a computer network architecture and/or a cognitive computing architecture that facilitates communications between various other devices and/or components. Thus, the aspects described herein address and solve issues of a technical nature that are necessarily rooted in computer technology.

For instance, aspects include analyzing various sources of data to predict whether a user will likely require a loan, the specific type of loan needed, and the specific amounts. In doing so, the aspects overcome issues associated with the inconvenience of manual and/or unnecessary monitoring of user input data by replacing manual procedures with a cognitive-based computing system. Without the improvements suggested herein, additional processing and memory usage would be required to perform such monitoring and/or to calculate the likelihood of various users require a loan.

Furthermore, the embodiments described herein function to personalize loans based upon an analysis of data that facilitates the impact of likely future event. The process improves upon existing technologies by more accurately forecasting such events may concurrently analyzing user input data from a larger number of sources than would be feasible or practical. As a result, the customization of loan specifics (e.g., the loan type, specific loan programs, loan terms, etc.) improves the speed, efficiency, and accuracy in which such calculations could otherwise be performed. Due to these improvements, the aspects address computer-related issues regarding efficiency over the traditional amount of processing power and models used to calculate loan specifics and/or perform data forecasting. Thus, the aspects also improve upon computer technology by requiring less calculations due to the increased efficiency provided, for example, by cognitive computing versus traditional computing techniques. For instance, because cognitive computing may be leveraged to determine whether a user requires a loan, this result reflects an improvement in accuracy and efficiency versus traditional computers, even assuming that non-cognitive computers could perform the tasks associated with the aspects disclosed herein. Therefore, the application of cognitive computers in particular allows for less calculations and less resource and power consumption than would otherwise be possible.

A First Exemplary Computer-Implemented Method for Predicting a User's Loan Needs and Presenting the User with a Loan Offer

In one aspect, a computer-implemented method for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The method may include one or more processors (and/or associated transceivers) (1) receiving user input data associated with a user; (2) generating a user profile based upon the user input data and being indicative of whether the user will require a loan within the future time period based upon the user profile; (3) calculating a statistical likelihood of the user requiring the loan within a future time period based upon the user profile; (4) determining whether the statistical likelihood exceeds a threshold likelihood; and/or (5) when the statistical likelihood exceeds a threshold likelihood, transmitting a notification to a computing device associated with the user including offers for one or more customized loans. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

For instance, in various aspects, the method may include calculating a range of loan amounts that have the statistically highest probability of being required by the user based upon the user profile, and preapproving the user for the highest amount within the range of loan amounts.

Additionally or alternatively, the user input data may include search terms and/or websites from the user's web browsing history relevant to the user requiring the loan. In such a case, the method may include receiving the user's web browsing history as part of the user input data, identifying, such search terms and websites from the user's web browsing history relevant to the user requiring the loan, and storing the relevant search terms and websites as part of the user's profile. In such aspects, the statistical likelihood of the user requiring the loan within the future time period may be based upon these relevant search terms and websites.

The user input data may indicate other types of relevant information used to predict whether a user requires a loan such as, for example, a number of life events and/or the current age of each member of the user's family. When the user input data includes data regarding the age of the user's family members, the method may additionally or alternatively use the user input data to determine a number of days until a member of the user's family turns legal driving age, and then determine that the statistical likelihood exceeds the threshold likelihood when the number of days is less than a minimum future time period

Furthermore, the transmission of the notification may include a notification being sent in various ways, sending one or more of (i) a text message, (ii) an email message, or (iii) a push notification, to a computing device associate with the user

Additionally or alternatively, the user may be one user from among several users that are monitored via the method. Thus, the method may additionally or alternatively include (i) calculating a statistical likelihood of each of the plurality of users requiring a loan within a future time period based upon each user's respective user profile; (ii) determining whether the statistical likelihood for each of the plurality of users exceeds a respective threshold likelihood; and (3) when the statistical likelihood for one of the plurality of users exceeds a respective threshold likelihood, transmitting a notification to a computing device associated with that particular user from among the plurality of users including offers for one or more customized loans.

A Second Exemplary Computer-Implemented Method for Predicting a User's Loan Needs and Presenting a Loan Offer

In another aspect, a computer-implemented method for predicting when a user requires a loan and presenting the user with one or more loan offers may be provided. The method may include one or more processors (1) receiving user input data associated with a user, such as from a user mobile or client device via wireless communication or data transmission over one or more radio links or wireless communication channels; (2) generating a user profile based upon the user input data and being indicative of whether the user will require a loan within a future time period; (3) calculating a statistical likelihood of the user requiring the loan within the future time period based upon the user profile; (4) determining whether the statistical likelihood exceeds a threshold likelihood; and/or (5) when the statistical likelihood exceeds a threshold likelihood, transmitting a notification to the user for one or more loan offers, such as to the user's mobile or client device via wireless communication or data transmission over one or more radio links or wireless communication channels. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

For instance, in various aspects, the method may include calculating a range of loan amounts associated with the loan that have the statistically highest probability of being required by the user based upon the user profile, and preapproving the user for the highest amount within the range of loan amounts.

Additionally or alternatively, the method may include receiving the user's web browsing history as part of the user input data, identifying search terms and websites from the user web browsing history relevant to the user requiring the loan, and storing the relevant search terms and websites as part of the user's profile. In this way, the statistical likelihood of the user requiring the loan within the future time period may be based upon the relevant search terms and websites.

Furthermore, the user input data may indicate a current age of each member of the user's family and/or a number of life events associated with the user. The method may additionally include determining a number of days until a member of the user's family turns legal driving age. Using this information, the method may determine that the statistical likelihood exceeds the threshold likelihood when the number of days is less than a minimum future time period.

The user may also be associated with a computing device, and in such a case the method may include transmitting the notification to the user by sending one or more of (i) a text message, (ii) an email message, or (iii) a push notification, to the computing device.

Additionally or alternatively, the user may be from among a plurality of users, and the method may include (i) calculating a statistical likelihood of each of the plurality of users purchasing requiring a loan within a future time period based upon each user's respective user profile; (ii) determining whether the statistical likelihood for each of the plurality of users exceeds a respective threshold likelihood; and (iii) when the statistical likelihood for one of the plurality of users exceeds a respective threshold likelihood, selectively transmitting a notification for one or more loan offers to that particular user from among the plurality of users

A Third Exemplary Computer-Implemented Method for Predicting a User's Loan Needs and Presenting the User with a Loan Offer

In another aspect, a computer-implemented method for predicting when a user requires a loan and presenting the user with one or more loan offers may be provided. The method may include one or more processors and/or transceivers (1) receiving user input data associated with a user from a user mobile device via wireless communication or data transmission over one or more radio links or wireless communication channels; (2) generating a user profile based upon the user input data and being indicative of whether the user will require a loan within a future time period; (3) calculating a statistical likelihood of the user requiring the loan within the future time period based upon the user profile; (4) determining whether the statistical likelihood exceeds a threshold likelihood; and/or (5) when the statistical likelihood exceeds the threshold likelihood, transmitting, by one or more processors and/or transceivers, a notification to the user mobile device for one or more loan offers via wireless communication or data transmission over one or more radio links or wireless communication channels to facilitate providing customized loans. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

A First Exemplary System for Predicting a User's Loan Needs and Presenting the User with a Loan Offer

In yet another aspect, a computer system for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The system may include (1) a client device (or mobile device) associated with a user, which may be configured to periodically transmit user input data associated with the user, such as via wireless communication or data transmission over one or more radio links or wireless communication channels; and (2) one or more back-end components configured to (i) receive the user input data, such as via wireless communication or data transmission over one or more radio links or wireless communication channels; (ii) generate a user profile based upon the received user input data and being indicative of whether the user will require a loan within a future time period; (iii) calculate a statistical likelihood of the user requiring a loan within the future time period based upon the user profile; (iv) determine whether the statistical likelihood exceeds a threshold likelihood; and/or (v) when the statistical likelihood exceeds a threshold likelihood, transmit a notification to the user client or mobile device for one or more loan offers, such as via wireless communication or data transmission over one or more radio links or wireless communication channels. The system may include additional, less, or alternate components, including those discussed elsewhere herein.

For instance, in various aspects, the one or more back-end components may be further configured to (i) calculate a range of loan amounts associated with the loan that have the statistically highest probability of being required by the user based upon the user profile; and (ii) preapprove the user for the highest amount within the range of loan amounts.

Additionally or alternatively, the client device may be further configured to transmit the user's web browsing history to the one or more back-end components as part of the user input data, and the one or more back-end components may be further configured to (i) identify search terms and websites from the user web browsing history relevant to the user requiring the loan, and (ii) store the relevant search terms and websites as part of the user's profile such that the statistical likelihood of the user requiring the loan within the future time period is based upon the relevant search terms and websites.

Furthermore, the user input data may indicate a current age of each member of the user's family and/or a number of life events associated with the user. The client device may be further configured to (i) determine a number of days until a member of the user's family turns legal driving age, and (ii) determine that the statistical likelihood exceeds the threshold likelihood when the number of days is less than a minimum future time period.

Additionally or alternatively, the one or more back-end components may be further configured to transmit the notification to the user for the one or more loan offers as one or more of (i) a text message, (ii) an email message, or (iii) a push notification.

Still further, aspects include the user being from among a plurality of users. In such aspects, the one or more back-end components may be further configured to (i) calculate a statistical likelihood of each of the plurality of users requiring a loan within a future time period based upon each user's respective user profile; (ii) determine whether the statistical likelihood for each of the plurality of users exceeds a respective threshold likelihood; and (iii) when the statistical likelihood for one of the plurality of users exceeds a respective threshold likelihood, transmit a notification for one or more loan offers to that particular user from among the plurality of users.

A Second Exemplary System for Predicting a User's Loan Needs and Presenting the User with a Loan Offer

In still another aspect, a computer system for predicting when a user requires a loan and presenting the user with a loan offer may be provided. The system may include (1) a mobile device associated with a user and configured to periodically transmit user input data associated with the user; and (2) one or more back-end components. The one or more back-end components may be configured to (i) receive the user input data transmitted by the mobile device via wireless communication or data transmission over one or more radio links or wireless communication channels; (ii) generate a user profile based upon the received user input data and being indicative of whether the user will require a loan within a future time period; (iii) calculate a statistical likelihood of the user requiring a loan within the future time period based upon the user profile; (iv) determine whether the statistical likelihood exceeds a threshold likelihood; and (v) when the statistical likelihood exceeds a threshold likelihood, transmit a notification to the user mobile device for user review via wireless communication or data transmission over one or more radio links or wireless communication channels, the notification detailing one or more loan offers, to facilitate providing customized loans. The system may include additional, less, or alternate components, including those discussed elsewhere herein.

Additional Considerations

This detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. One may be implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this application.

Further to this point, although the aspects described herein often utilize credit report information as an example of sensitive information, the aspects described herein are not limited to such examples. Instead, the aspects described herein may be implemented in any suitable environment in which it is desirable to identify and control specific type of information. For example, the aforementioned aspects may be implemented by a financial institution to identify and contain bank account statements, brokerage account statements, tax documents, etc. To provide another example, the aforementioned aspects may be implemented by a lender to not only identify, re-route, and quarantine credit report information, but to apply similar techniques to prevent the dissemination of loan application documents that are preferably delivered to a client for signature in accordance with a more secure means (e.g., via a secure login to a web server) than via email.

Furthermore, although the present disclosure sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the words of the claims set forth at the end of this patent and equivalents. The detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical. Numerous alternative embodiments may be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims. Although the following text sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the words of the claims set forth at the end of this patent and equivalents. The detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical. Numerous alternative embodiments may be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.

The following additional considerations apply to the foregoing discussion. Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

Additionally, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, the routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.

Hardware modules may provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information).

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.

The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. For example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the description. This description, and the claims that follow, should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.

The patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s).

The various systems and methods described herein are directed to an improvement to computer functionality, and improve the functioning of conventional computers, as described, for example, in the “Technical Advantages” Section and elsewhere herein. 

1. A computer-implemented method comprising: training, by one or more processors, a machine learning model to determine a set of weights based on historical profiles associated with a set of users, wherein the set of weights indicate correlations between: a set of user inputs indicated in the historical profiles, and instances of individual users, in the set of users, obtaining loans; receiving, by the one or more processors, user input data associated with a user; generating, by the one or more processors, a user profile based upon the user input data; predicting, by the one or more processors and using the machine learning model, a statistical likelihood that the user will require a loan within a future time period, by: identifying at least one user input, of the set of user inputs, indicated in the user profile; determining at least one weight, of the set of weights, associated with the at least one user input; and determining the statistical likelihood based on the at least one weight; determining, by the one or more processors, that the statistical likelihood exceeds a threshold likelihood; and based at least in part on the statistical likelihood exceeding the threshold likelihood, transmitting, by the one or more processors, a notification to a computing device associated with the user, wherein the notification includes one or more offers for one or more customized loans.
 2. The computer-implemented method of claim 1, further comprising: calculating, by the one or more processors and based at least in part on the user profile, a set of probabilities associated with a range of loan amounts, wherein probabilities in the set of probabilities indicate respective likelihoods that the loan will be for corresponding loan amounts; identifying, by the one or more processors, a loan amount associated with a highest probability of the set of probabilities; and preapproving, by the one or more processors, the user for the loan amount.
 3. The computer-implemented method of claim 1, further comprising: receiving, by the one or more processors as part of the user input data, a web browsing history associated with the user; identifying, by the one or more processors, one or more of search terms and websites from the web browsing history that are relevant to the user; and storing, by the one or more processors, the one or more of the search terms and websites as part of the user profile, wherein the statistical likelihood is based at least in part upon the one or more of the search terms and websites.
 4. The computer-implemented method of claim 1, wherein the user input data indicates a current age of a family member of the user, the method further comprising: determining, by the one or more processors and based at least in part on the current age, a number of days until the family member reaches a legal driving age; and determining, by the one or more processors, that the statistical likelihood exceeds the threshold likelihood based at least in part on the number of days being less than a threshold number of days.
 5. The computer-implemented method of claim 1, wherein the user input data indicates one or more life events associated with the user.
 6. The computer-implemented method of claim 1, wherein the notification is one or more of: (i) a text message, (ii) an email message, or (iii) a push notification.
 7. (canceled)
 8. A computer system comprising: one or more processors; memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to: train a machine learning model to determine a set of weights based on historical profiles associated with a set of users, wherein the set of weights indicates correlations between: a set of user inputs indicated in the historical profiles, and instances of individual users, in the set of users, obtaining loans; receive user input data from a client device associated with a user; generate a user profile based upon the user input data; predict, using the machine learning model, a statistical likelihood that the user will require a loan within a future time period, by identifying at least one user input, of the set of user inputs, indicated in the user profile; determining at least one weight, of the set of weights, associated with the at least one user input; and determining the statistical likelihood based on the at least one weight; determine that the statistical likelihood exceeds a threshold likelihood; and based at least in part on the statistical likelihood exceeding the threshold likelihood, transmit a notification to the client device, wherein the notification includes one or more offers for one or more customized loans.
 9. The computer system of claim 8, wherein the computer-executable instructions further cause the one or more processors to: calculate, based at least in part on the user profile, a set of probabilities associated with a range of loan amounts, wherein probabilities in the set of probabilities indicate respective likelihoods that the loan will be for corresponding loan amounts; identify a loan amount associated with a highest probability of the set of probabilities; and preapprove the user for the loan amount.
 10. The computer system of claim 8, wherein: the user input data includes a web browsing history associated with the user, the computer-executable instructions further cause the one or more processors to: (i) identify one or more of search terms and websites from the web browsing history that are relevant to the user requiring the loan, and (ii) store the one or more of the search terms and websites as part of the user profile, and the statistical likelihood is based at least in part upon the one or more of the search terms and websites.
 11. The computer system of claim 8, wherein the user input data indicates a current age of a family member of the user, and the computer-executable instructions further cause the one or more processors to: (i) determine, based at least in part on the current age, a number of days until the family member reaches a legal driving age, and (ii) determine that the statistical likelihood exceeds the threshold likelihood based at least in part on when the number of days being less than a threshold number of days.
 12. The computer system of claim 8, wherein the user input data indicates one or more life events associated with the user.
 13. The computer system of claim 8, wherein the notification is one or more of: (i) a text message, (ii) an email message, or (iii) a push notification.
 14. (canceled)
 15. A non-transitory computer-readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising: training a machine learning model to determine a set of weights based on historical profiles associated with a set of users, wherein the set of weights indicate correlations between: a set of user inputs indicated in the historical profiles, and instances of individual users, in the set of users, obtaining loans; receiving user input data associated with a user from a mobile device of the user; generating a user profile based upon the user input data; predicting, using the machine learning model, a statistical likelihood that the user will require a loan within a future time period, by: identifying at least one user input, of the set of user inputs, indicated in the user profile; determining at least one weight, of the set of weights, associated with the at least one user input; and determining the statistical likelihood based on the at least one weight; determining that the statistical likelihood exceeds a threshold likelihood; and based at least in part on the statistical likelihood exceeding the threshold likelihood, transmitting a notification to the mobile device, wherein the notification includes one or more offers for one or more customized loans.
 16. The non-transitory computer-readable medium of claim 15, wherein the operations further comprise: receiving, as part of the user input data, a web browsing history associated with the user; identifying one or more of search terms and web sites from the web browsing history that are relevant to the user requiring the loan; and storing the one or more of the search terms and web sites as part of the user profile, wherein the statistical likelihood is based at least in part upon the one or more of the search terms and websites.
 17. The non-transitory computer-readable medium of claim 15, wherein the user input data indicates a current age of a family member of the user, and the operations further comprise: determining, based at least in part on the current age, a number of days until the family member reaches a legal driving age; and determining that the statistical likelihood exceeds the threshold likelihood based at least in part on the number of days being less than a threshold number of days.
 18. The non-transitory computer-readable medium of claim 15, wherein the user input data indicates one or more life events associated with the user.
 19. (canceled)
 20. (canceled)
 21. The computer-implemented method of claim 1, wherein the computing device associated with the user is a mobile device, and the one or more processors receives the user input data from the mobile device.
 22. The computer-implemented method of claim 1, further comprising determining, by the one or more processors, at least one of a loan type, a loan term, and a monetary amount associated with the one or more customized loans.
 23. The computer-implemented method of claim 1, wherein the threshold likelihood comprises a first threshold likelihood, the method further comprising: determining, by the one or more processors, that the statistical likelihood exceeds a second threshold likelihood, the second threshold likelihood being higher than the first threshold likelihood; and pre-approving, by the one or more processors, the user for the one or more customized loans based on determining that the statistical likelihood exceeds the second threshold likelihood.
 24. The computer-implemented method of claim 1, further comprising: determining, by the one or more processors, that the at least one user input corresponds to a predetermined condition associated with a particular type of loan; and determining, by the one or more processors, that the statistical likelihood exceeds the threshold likelihood, based on determining that the at least one user input corresponds to the predetermined condition, wherein the notification includes an offer associated with the particular type of loan. 